Meta Interview Questions

Meta Data Manipulation (SQL/Python) Interview Questions

Practice 1,159 real Meta interview questions for 2026. Covers top categories — Coding & Algorithms, Analytics & Experimentation, Data Manipulation (SQL/Python), Behavioral & Leadership, and System Design — across Software Engineer, Data Scientist, Machine Learning Engineer, Data Engineer, and Product Manager roles. Real questions from actual interviews with detailed solutions. Expect a software-engineering-heavy loop: timed algorithmic coding (trees, arrays, graph/maze problems, delimiter/CSV parsing), system-design prompts like leaderboards, flight search and online-judge architectures, and an increasingly common AI-assisted coding round that mirrors real workflows. Data Scientist rounds emphasize product analytics and experimentation—designing tests, diagnosing spend drops and bots, evaluating unconnected content, and writing SQL for multi-account, seller, and vehicle metrics. Machine Learning Engineer questions skew toward recommender and ranking work (place and friend recommendation, sparse-matrix ops, linear-regression derivations, newsfeed dislike models). Data Engineers focus on data modeling, ETL, capacity calculations, reservations/utilization queries, and production SQL/Python tasks. For interview preparation, prioritize timed coding practice, system-design templates, rigorous SQL drills (joins/CTEs/aggregation), clear A/B-testing frameworks, and concise STAR behavioral stories tied to measurable impact.

1.2k Questions 1 Company07.06.2026
Showing 20 results
Role
Meta logo
Meta
Hard
Data Scientist

Compute sample size and test duration correctly

Powering Two Online Experiments: Sample Size, Duration, and Design Defenses You are designing experiments to improve a friend-accept rate metric in a ...

Statistics & Math
3
0
59 people solved
Oct 13, 2025
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Meta
Medium
Software Engineer

Merge Two Lists of Non-Overlapping Intervals

You are given two lists of closed integer intervals, a and b. Each interval is represented as a pair [start, end] with start <= end, and it covers eve...

Coding & Algorithms
1
0
13 people solved
Mar 6, 2026
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Meta
Medium
Software Engineer

Solve peak element and unique word abbreviation

You are given two independent coding problems to solve. 1) Peak element in an array (binary search) - Input: an integer array nums of length n >= 1. -...

Coding & Algorithms
2
0
38 people solved
Feb 6, 2026
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Meta
Medium
Machine Learning Engineer

Answer core behavioral questions using STAR

Prepare structured answers (use STAR: Situation, Task, Action, Result) for the following common behavioral prompts: 1. Most proud project: Describe a ...

Behavioral & Leadership
7
0
67 people solved
Dec 15, 2025
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Meta
Medium
Machine Learning Engineer

Answer impact, conflict, and difficult coworker questions

Behavioral questions 1. Describe the most impactful project you have worked on. 2. Tell me about a difficult person you have worked with. 3. Describe ...

Behavioral & Leadership
4
0
62 people solved
Dec 15, 2025
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Meta
Medium
Data Scientist

Resolve Conflicts and Convince Skeptical Stakeholders Effectively

Resolve Conflicts and Convince Skeptical Stakeholders Effectively Scenario You are an IC-level data scientist (IC5-or-below) working on fast-paced, cr...

Behavioral & Leadership
70
0
270 people solved
Aug 4, 2025
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Meta
Hard
Machine Learning EngineerSenior+ AI Locked

Find Maximum Unique-Character Subset

This question evaluates algorithm design and combinatorial optimization skills, specifically the ability to model disjoint-character constraints, hand...

Coding & Algorithms
2
0
47 people solved
Mar 1, 2026
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Meta
Medium
Data Engineer Locked

Define metrics and data model for product features

This question evaluates a candidate's product sense and data engineering competency, including defining core success metrics, designing dashboard visu...

System Design
16
0
131 people solved
Mar 1, 2026
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Meta
Medium
Data Scientist

Compute seller counts and vehicle share

You are given two tables: 1. listing_interactions - buyer_id BIGINT - seller_id BIGINT - event_date DATE - product_id BIGINT - listing_...

Data Manipulation (SQL/Python)
6
0
51 people solved
Jan 5, 2026
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Meta
Easy
Machine Learning EngineerSenior+ Locked

Solve Tree Views, Columns, and Calculator

This multi-part question evaluates skills in binary tree traversal and view extraction, vertical column grouping and ordering of tree nodes, and parsi...

Coding & Algorithms
3
0
35 people solved
Feb 27, 2026
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Meta
Hard
Software Engineer

Design an in-memory cloud storage system

In-Memory Cloud Storage Service (Take-home) Design and implement an in-memory cloud storage service that maps files (objects) to their metadata. The s...

System Design
17
0
238 people solved
Sep 6, 2025
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Meta
Medium
Machine Learning Engineer

Describe handling intense time pressure

Behavioral & Leadership (Onsite): Thriving Under Time Pressure and Multitasking Prompt Tell me about a time you had to deliver high‑quality work under...

Behavioral & Leadership
11
0
82 people solved
Sep 6, 2025
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Meta
Medium
Software Engineer

Design a post privacy/visibility system

Design a privacy system for social media posts (similar to Facebook post privacy). Requirements: - Each post can be: Public, Friends, Friends-of-frien...

System Design
5
0
49 people solved
Feb 25, 2026
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Meta
Medium
Software Engineer

Design a chat messaging system

Design a chat system similar to WhatsApp/Messenger. Requirements: - 1:1 and group chats. - Send/receive messages in real time when online; store-and-f...

System Design
6
0
43 people solved
Feb 25, 2026
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Meta
Medium
Software Engineer

Design a ticket or hotel reservation system

Design a reservation system (Ticketmaster-style event seats or hotel rooms). Requirements: - Users search availability by date/event. - Users select s...

System Design
2
0
43 people solved
Feb 25, 2026
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Meta
Medium
Software Engineer

Design real-time live comments

Design a real-time comment system for live video (e.g., Facebook Live comments). Requirements: - Viewers post comments; all viewers see new comments q...

System Design
5
0
40 people solved
Feb 25, 2026
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Meta
Medium
Software Engineer

Design an ad click aggregation service

Design a backend service that ingests ad impression and click events and provides aggregated metrics. Requirements: - Ingest a high-volume stream of e...

System Design
3
0
43 people solved
Feb 25, 2026
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Meta
Hard
Product Manager

Meta PM Interview Questions

Product and Decision-Making Onsite Case Prompt Set You are a Product Manager candidate preparing for a mixed onsite loop covering strategy, data, desi...

Product / Decision Making
107
0
894 people solved
Jul 4, 2025
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Meta
Hard
Machine Learning EngineerSenior+

Design image and multimodal generation systems

System Design: Image Generation and Multimodal Generation Part 1 — End-to-End Image Generation System Design an end-to-end image generation system. Co...

ML System Design
9
0
102 people solved
Aug 11, 2025
Meta logo
Meta
Medium
Data Scientist

Evaluate AI-assisted ad creation

Meta is considering launching an AI-assisted ad creation feature for advertisers. The feature helps advertisers generate ad copy and/or creatives insi...

Analytics & Experimentation
26
0
183 people solved
Feb 23, 2026

Frequently Asked Questions

How difficult are Meta interview questions?
Meta interview questions span a wide difficulty range because they must screen candidates from entry to senior levels across many functions. Expect coding rounds to map to medium-to-hard algorithmic problems that appear in top 100 problem lists for software engineers, and expect data roles to face challenging SQL, experiment diagnosis, and product-analytics problems that require clean metric definitions. Machine learning roles emphasize recommendation and ranking tradeoffs and model complexity, while data engineers encounter large-scale ETL and modeling puzzles. Difficulty scales with level: entry hires see clearer, bounded problems; senior hires face ambiguous tradeoffs and system-wide thinking.
What is Meta's interview process and where do these questions appear?
Meta typically runs a multi-stage process: recruiter screen, one or two technical screens or an online assessment, a full loop of onsite-style interviews, then debrief, committee review, and offer. The full loop mixes coding, system or product design, role-specific technical rounds, and behavioral interviews. Software-engineer candidates spend most time on coding and design; data scientists focus on SQL, experimentation, and product analytics; machine-learning engineers see modeling and recommendation design; data engineers handle SQL, data modeling, and pipeline questions; PMs get product-design and analytics probes. In 2025–2026 some teams pilot AI-enabled coding rounds.
How should I structure a preparation timeline for a Meta interview?
A focused six-week plan works well: weeks one and two cover fundamentals—data structures, algorithms, SQL basics, and experiment design; weeks three and four emphasize timed problem practice, mock phone screens, and role-specific cases (A/B diagnosis for data scientists, model design for MLEs, ETL modeling for data engineers); week five concentrates on system or product design and behavioral storytelling; week six is for full mock loops, timing, and refining communication. Practice with realistic tools, simulate loop pacing, and schedule a debrief after each mock to iterate on clarity, edge-case handling, and time management.
Which technical subtopics are most commonly tested for each role at Meta?
For Data Scientist interviews the recurring technical themes are product-metric definition, diagnosing experiment and spend drops, counting multi-account interactions, SQL for multi-entity metrics, and ranking or recommendation evaluation such as shop ad ranking. Software-engineer questions frequently focus on timestamped state and versioned systems, leaderboards and ranking, maze/graph traversal and tree/array transforms, delimiter and CSV parsing, and scalable search or flight-search style designs. Machine-learning engineers see place and friend recommendation design, sparse-matrix operations, ranking/loss choices, and feed dislike or personalization models. Data engineers repeatedly face entity modeling for feed and booking data, SQL analytics for utilization and reservations, and capacity-aware aggregation challenges.
What standout tips and common pitfalls should I watch for in Meta interviews?
Start interviews by clarifying requirements and expected outputs, then propose measurable success metrics; this prevents misaligned solutions. For coding, think aloud, handle edge cases, state complexity up front, and write a couple of quick tests. In design rounds quantify load, storage, and tradeoffs rather than vague features. Data roles must define metrics, guardrails, and experiment assumptions before jumping to analysis; common pitfalls are ambiguous metric definitions, peeking at tests, and ignoring instrumentation limits. For AI-assisted coding rounds, use the assistant to accelerate boilerplate but validate logic and corner cases yourself. Finish each answer with a concise summary of impact and tradeoffs.

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